Logistics Innovation and Social Sustainability: How to Prevent an Artificial Divide in Human–Computer Interaction

Human–computer interaction (HCI) is a cornerstone for the success of technical innovation in the logistics and supply chain sector. As a major part of social sustainability, this interaction is changing as artificial intelligence applications (Internet of Things, autonomous transport, Physical Internet) are implemented, leading to larger machine autonomy, and hence the transition from a primary executive to a supervisory role of human operators. A fundamental question concerns the level of control transferred to machines, such as autonomous vehicles and automatic materials handling devices. Problems include a lack of human trust toward automatic decision making or an inclination to override the system in case automated decisions are misperceived. This paper outlines a theoretical framework, describing different levels of acceptance and trust as a key HCI element of technology innovation, and points to the possible danger of an artificial divide at both the individual and firm level. Based upon the findings of four benchmark cases, a classification of the roles of human employees in adopting innovations is developed. Measures at operational, tactical, and strategic level are discussed to improve HCI, more in particular the capacity of individuals and firms to apply state-of-the-art techniques and to prevent an artificial divide, thereby increasing social sustainability.

[1]  Li Deng,et al.  Artificial Intelligence in the Rising Wave of Deep Learning: The Historical Path and Future Outlook [Perspectives] , 2018, IEEE Signal Processing Magazine.

[2]  Elkafi Hassini,et al.  Fulfillment source allocation, inventory transshipment, and customer order transfer in e-tailing , 2015 .

[3]  Chun-Wei Yang,et al.  Applications of artificial intelligence in intelligent manufacturing: a review , 2017, Frontiers of Information Technology & Electronic Engineering.

[4]  Angappa Gunasekaran,et al.  Embedded devices for supply chain applications: Towards hardware integration of disparate technologies , 2014, Expert Syst. Appl..

[5]  Anastasios A. Economides,et al.  Mobile-Based Assessment: Integrating acceptance and motivational factors into a combined model of Self-Determination Theory and Technology Acceptance , 2017, Comput. Hum. Behav..

[6]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[7]  Uday Venkatadri,et al.  Physical Internet, conventional and hybrid logistic systems: a routing optimisation-based comparison using the Eastern Canada road network case study , 2017, Int. J. Prod. Res..

[8]  Shuzhu Zhang,et al.  Swarm intelligence applied in green logistics: A literature review , 2015, Eng. Appl. Artif. Intell..

[9]  Tai Gyu Kim,et al.  Change-Supportive Employee Behavior: Antecedents and the Moderating Role of Time , 2011 .

[10]  B. Lahno On the Emotional Character of Trust , 2001 .

[11]  Rino Falcone,et al.  Trust and control: A dialectic link , 2000, Appl. Artif. Intell..

[12]  Hugo Horta,et al.  Training students for new jobs: The role of technical and vocational higher education and implications for science policy in Portugal , 2016 .

[13]  Wendy Ju,et al.  Why did my car just do that? Explaining semi-autonomous driving actions to improve driver understanding, trust, and performance , 2014, International Journal on Interactive Design and Manufacturing (IJIDeM).

[14]  Elliot Bendoly,et al.  Fit, Bias, and Enacted Sensemaking in Data Visualization: Frameworks for Continuous Development in Operations and Supply Chain Management Analytics , 2016 .

[15]  Johannes Weyer,et al.  Human–Machine Cooperation in Smart Cars: An Empirical Investigation of the Loss-of-Control Thesis , 2015 .

[16]  Gunther Reinhart,et al.  Predicting Future Inbound Logistics Processes Using Machine Learning , 2016 .

[17]  Akinori Nakata,et al.  NIOSH national survey of long-haul truck drivers: Injury and safety. , 2015, Accident; analysis and prevention.

[18]  Vedat Verter,et al.  A lead-time based approach for planning rail-truck intermodal transportation of dangerous goods , 2010, Eur. J. Oper. Res..

[19]  Benjamin T. Hazen,et al.  Toward creating competitive advantage with logistics information technology , 2012 .

[20]  Katja Kircher,et al.  Interface design of eco-driving support systems – Truck drivers’ preferences and behavioural compliance , 2015 .

[21]  Gilles Coppin,et al.  The TAPAS Project: Facilitating Cooperation in Hybrid Combat Air Patrols Including Autonomous UCAVs , 2015 .

[22]  Russell G. Thompson,et al.  Trucks and bikes: sharing the roads , 2014 .

[23]  Allan F. Williams,et al.  A Review of Hazard Anticipation Training Programs for Young Drivers. , 2015, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[24]  ManMohan S. Sodhi,et al.  Conceptualizing Social Responsibility in Operations Via Stakeholder Resource‐Based View , 2015 .

[25]  Benjamin T. Hazen,et al.  Supply chain social sustainability for developing nations: Evidence from India , 2016 .

[26]  Kap Hwan Kim,et al.  Negotiating truck arrival times among trucking companies and a container terminal , 2015 .

[27]  Huang Chen,et al.  Building a Belief–Desire–Intention Agent for Modeling Neural Networks , 2015, Appl. Artif. Intell..

[28]  Jacqueline M. Bloemhof,et al.  Sustainability assessment of food chain logistics , 2015 .

[29]  Wendy L. Tate,et al.  Sustainable Supply Chain Design in Social Businesses: Advancing the Theory of Supply Chain , 2018 .

[30]  Fred D. Davis,et al.  A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies , 2000, Management Science.

[31]  Xu Zhou,et al.  Data Mining for Vehicle Telemetry , 2016, Appl. Artif. Intell..

[32]  Angappa Gunasekaran,et al.  Expert systems and artificial intelligence in the 21st century logistics and supply chain management , 2014, Expert Syst. Appl..

[33]  Bogna Mrówczyńska,et al.  Application of Artificial Intelligence in Prediction of Road Freight Transportation , 2017 .

[34]  G. H. Walker,et al.  21st century trucking: A trajectory for ergonomics and road freight. , 2016, Applied ergonomics.

[35]  Andreas Norrman,et al.  The physical internet – review, analysis and future research agenda , 2017 .

[36]  Eleonora Bottani,et al.  An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses , 2017, Eur. J. Oper. Res..

[37]  Benoît Montreuil,et al.  Toward a Physical Internet: meeting the global logistics sustainability grand challenge , 2011, Logist. Res..

[38]  A. Michael Knemeyer,et al.  Motivations for environmental and social consciousness: Reevaluating the sustainability-based view , 2017 .

[39]  Katsumi Morikawa,et al.  Reprint "Efficient flexible long-term capacity planning for optimal sustainability dimensions performance of reverse logistics social responsibility: A system dynamics approach" , 2017 .

[40]  G. Broman,et al.  A strategic approach to social sustainability - Part 1: exploring the social system , 2017 .

[41]  Vera Hummel,et al.  Decentralized Control of Logistic Processes in Cyber-physical Production Systems at the Example of ESB Logistics Learning Factory , 2016 .

[42]  Xianpeng Wang,et al.  A machine-learning based memetic algorithm for the multi-objective permutation flowshop scheduling problem , 2017, Comput. Oper. Res..

[43]  William J. Rose,et al.  Crowdsourcing Last Mile Delivery: Strategic Implications and Future Research Directions , 2018 .

[44]  Natasha Merat,et al.  Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions , 2013 .

[45]  Mark R Lehto,et al.  Classifying injury narratives of large administrative databases for surveillance-A practical approach combining machine learning ensembles and human review. , 2017, Accident; analysis and prevention.

[46]  Jasmine Pahukula,et al.  A time of day analysis of crashes involving large trucks in urban areas. , 2015, Accident; analysis and prevention.

[47]  Ricardo Giesen,et al.  Real-time prediction of bus travel speeds using traffic shockwaves and machine learning algorithms , 2016 .

[48]  Göran Broman,et al.  A Strategic Approach to Social Sustainability - Part 2 : A Principle-based Definition , 2017 .

[49]  Horst Tempelmeier,et al.  Capacitated dynamic production and remanufacturing planning under demand and return uncertainty , 2016, OR Spectr..

[50]  Ali Harimi,et al.  Anger or Joy? Emotion Recognition Using Nonlinear Dynamics of Speech , 2015, Appl. Artif. Intell..

[51]  Gönenç Gürkaynak,et al.  Stifling artificial intelligence: Human perils , 2016, Comput. Law Secur. Rev..

[52]  A. Schneider Reflexivity in Sustainability Accounting and Management: Transcending the Economic Focus of Corporate Sustainability , 2014, Journal of Business Ethics.

[53]  Colin Camerer,et al.  Not So Different After All: A Cross-Discipline View Of Trust , 1998 .

[54]  F. Mannering,et al.  Differences in rural and urban driver-injury severities in accidents involving large-trucks: an exploratory analysis. , 2005, Accident; analysis and prevention.

[55]  Matthias Klumpp,et al.  Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements , 2018 .

[56]  Mohammad Jafar Tarokh,et al.  Trust Prediction in Online Communities Employing Neurofuzzy Approach , 2015, Appl. Artif. Intell..

[57]  Luca Bertazzi,et al.  Dynamic expediting of an urgent order with uncertain progress , 2018, Eur. J. Oper. Res..

[58]  Sergio Toral,et al.  The moderating role of prior experience in technological acceptance models for ubiquitous computing services in urban environments , 2015 .

[59]  Jaime Cerdá,et al.  The heterogeneous vehicle routing and truck scheduling problem in a multi-door cross-dock system , 2015, Comput. Chem. Eng..

[60]  Tucker McElroy,et al.  The Multivariate Bullwhip Effect , 2016, Eur. J. Oper. Res..

[61]  Joseph Sarkis,et al.  Sustainability and supply chain management – An introduction to the special issue , 2008 .

[62]  Ray Y. Zhong,et al.  Big Data Analytics for Physical Internet-based intelligent manufacturing shop floors , 2017, Int. J. Prod. Res..

[63]  Hichem Omrani,et al.  Predicting Travel Mode of Individuals by Machine Learning , 2015 .

[64]  Matthias Klumpp,et al.  Artificial Divide: The New Challenge of Human-Artificial Performance in Logistics , 2017 .

[65]  Hiroyuki Aburatani,et al.  Abstract B1-08: Deep learning for the large-scale cancer data analysis , 2015 .

[66]  David E. Cantor,et al.  Maximizing the Potential of Contemporary Workplace Monitoring: Techno‐Cultural Developments, Transactive Memory, and Management Planning , 2016 .

[67]  Agostino Nuzzolo,et al.  A system of models to forecast the effects of demographic changes on urban shop restocking , 2014 .

[68]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[69]  Ellen Enkel,et al.  Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices , 2016 .

[70]  B. Fischhoff,et al.  How safe is safe enough? A psychometric study of attitudes towards technological risks and benefits , 1978 .

[71]  F. Sahin,et al.  A model of supply chain and supply chain decision‐making complexity , 2011 .

[72]  George Q. Huang,et al.  Physical Internet and interconnected logistics services: research and applications , 2017, Int. J. Prod. Res..

[73]  Teodor Gabriel Crainic,et al.  Physical Internet Enabled Hyperconnected City Logistics , 2016 .

[74]  Hao Luo,et al.  Scheduling at an auction logistics centre with physical internet , 2016 .

[75]  Patrick Doherty,et al.  A Modeling Framework for Troubleshooting Automotive Systems , 2016, Appl. Artif. Intell..

[76]  Otthein Herzog,et al.  Machine learning in agent-based stochastic simulation: Inferential theory and evaluation in transportation logistics , 2012, Comput. Math. Appl..

[77]  Ana Paula Barbosa-Póvoa,et al.  Planning a sustainable reverse logistics system: Balancing costs with environmental and social concerns , 2014 .

[78]  Wim H. Gijselaers,et al.  Bringing Learning to the Workplace: A Smartphone App for Reflection and Increased Authenticity of Learning , 2015 .

[79]  Hanna Maoh,et al.  Classifying the purpose of stopped truck events: An application of entropy to GPS data , 2016 .

[80]  Chang-Soo Han,et al.  Human–robot cooperation control based on a dynamic model of an upper limb exoskeleton for human power amplification , 2014 .

[81]  Pik-Yin Mok,et al.  Intelligent product cross-selling system with radio frequency identification technology for retailing , 2012 .